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https://github.com/neeraj3029/chi-sq-test
npm package to run Chi-Squared tests on numerical arrays.
https://github.com/neeraj3029/chi-sq-test
chi-square chi-squared-test distribution npm statistics
Last synced: about 2 months ago
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npm package to run Chi-Squared tests on numerical arrays.
- Host: GitHub
- URL: https://github.com/neeraj3029/chi-sq-test
- Owner: neeraj3029
- License: mit
- Created: 2020-11-15T20:50:29.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-02-12T17:50:36.000Z (almost 3 years ago)
- Last Synced: 2024-10-05T11:05:46.609Z (3 months ago)
- Topics: chi-square, chi-squared-test, distribution, npm, statistics
- Language: TypeScript
- Homepage: https://www.npmjs.com/package/chi-sq-test
- Size: 562 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Chi-Sqaured tests
This package helps run Chi-Squared hypothesis tests for testing distributions on numerical data. Details follow.
### How to install
This library can easily be integrated in to your project manually. Alternatively, the library can be included using npm/GitHub packages.
```console
npm install chi-sq-test
```
### How to use
**To run chi-squared test for a given dataset**
```console
ChiSqTest.gof(fObs, fExp, ddof)
```
Documentation
-
fObs: [Array] Array of observed frequencies for each category
Default: No default value, essential arg
-
fExp: [Array] Array of expected frequencies in each category
Default: By default all categories are assumed to be equally likely. Expected frequency of each category would be the mean of observed frequencies.
-
ddof: [number] delta degrees of freedom.
Effective degrees of freedom =k - 1 - ddof, where k is the number of observed frequencies.
Default ddof: 0
This is somewhat similar to SciPy.
#### Example:
```js
const ChiSqTest = require('chi-sq-test');
const obs = [2, 3, 4]; // observed frequencies
const exp = [3, 4, 5]; // expected frequencies
const ddof = 0; // delta degree of freedom (Degree Of Freedom = 3-1 = 2)
const testres1 = ChiSqTest.gof(obs, exp, ddof);
console.log(testres1);
/*
=> { value: 0.7833333333333332, pValue: 0.6759293959125221 }
*/
const testres2 = ChiSqTest.gof(obs); // mean fObs is used as fExp by default
console.log(testres2);
/*
=> { value: 0.6666666666666666, pValue: 0.7165313783925148 }
*/
```
#### Output:
Function ```gof``` returns a JSON object, which contains Chi-Square `value` and the `pValue` for the given dataset.
## Chi-square statistics for independence
```console
ChiSqTest.independence(fObs, ddof)
```
Documentation
-
fObs: [2D Array] 2D-Array of observed frequencies of interestcting categories Tij = (Ai ∩ Bj)
Default: No default value, essential arg
-
ddof: [number] delta degrees of freedom.
Effective degrees of freedom =(k - 1).(m - 1) - ddof, where k and m are number of categories in sets A and B respectively.
Default ddof: 0
#### Example
Statement
We have an email-dataset which is divided in two ways. \
A = {with image, without images} \
B = {Spam, No Spam}
fObs(i,j)
With Images
Without Images
Spam
160
240
No Spam
140
460
For the null hypothesis: \
H0: Email spam and image attachment are independent. \
HA: being spam and image attachment are dependent
```js
const ChiSqTest = require('chi-sq-test');
const obs = [
[160, 240],
[140, 460]
];
console.log(
ChiSqTest.independence(obs, 0)
);
/*
=> { value: 31.746031746031747, pValue: 1.7570790822318827e-8 }
*/
```
**Output**:
Function ```independence``` returns a JSON object, which contains Chi-Square `value` and the `pValue` for the hypothesis for indpendence.